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Proxy Variables and Nonparametric Identification of Causal Effects


  • de Luna, Xavier

    (Umeå University)

  • Fowler, Philip

    (Umeå University)

  • Johansson, Per

    (Uppsala University)


Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcome framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.

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  • de Luna, Xavier & Fowler, Philip & Johansson, Per, 2016. "Proxy Variables and Nonparametric Identification of Causal Effects," IZA Discussion Papers 10057, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp10057

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    References listed on IDEAS

    1. Frost, Peter A, 1979. "Proxy Variables and Specification Bias," The Review of Economics and Statistics, MIT Press, vol. 61(2), pages 323-325, May.
    2. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    3. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
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    More about this item


    potential outcomes; observational studies; average treatment effect; unobserved confounders;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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